Artificial intelligence and engineering education

Johri, Aditya

doi:10.1002/jee.20326

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…Such tools would assess students' ability to grapple with uncertainties in engineering work and correspondingly provide instructors a broad understanding of the engineering decisions that teams are making. While automated coding is necessary for widespread implementation, the high reliability obtained in this study combined with ongoing advances in lexical systems (Ha et al, 2011), machine learning (Jescovitch et al, 2020), and artificial intelligence (Johri, 2020;Roll & Wylie, 2016) make this aim appear tenable. Engagement of researchers and developers from these communities is needed.…”

Section: Limitationsmentioning

confidence: 96%

Aligning classroom assessment with engineering practice: A design‐based research study of a two‐stage exam with authentic assessment

Koretsky

McColley

Gugel

et al. 2021

J of Engineering Edu

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Background: Authentic assessment and two-stage exams have recently received attention; however, they are rarely used together. We reimagine assessment by integrating an authentic, computer-based assessment into the structure of a two-stage exam in a large engineering class. Purpose: We seek to identify ways that such assessment extends classroom testing to better align with engineering practice by examining the ways teams negotiate uncertainty to make engineering decisions. We also identify differing students' reactions to increased uncertainty during tests.Design/Method: Using the methodical framework of design-based research, we analyze performance and reflection data for 117 student teams through two design iterations to explore four design and theoretical conjectures. Results: Teams chose multiple solution paths to this authentic task, an aspect that aligns with the characteristics of engineering practice that we seek to assess. In addition, the technology tool allows the evaluation of procedural accuracy for many of the teams' chosen paths. The teams' decision-making performances correlate; however, decision-making and traditional assessments do not correlate, suggesting they measure different competencies. The computerbased second stage provides a holistic assessment that shifts the messages that students implicitly receive about valued practices in the classroom. However, not all students took up the authentic group assessment in desired ways.Conclusions: Technology-based two-stage exams with authentic assessment show promise to shift testing practices in large engineering classes to include decision-making. Such assessments better align with engineering practices that are valued in the profession, but more work is needed to develop systems for widespread implementation.

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Section: Limitationsmentioning

confidence: 96%

Aligning classroom assessment with engineering practice: A design‐based research study of a two‐stage exam with authentic assessment

Koretsky

McColley

Gugel

et al. 2021

J of Engineering Edu

View full text Add to dashboard Cite

show abstract

“…Already, we see many AI or ML based applications in education (Zhang & Aslan, 2021). For instance, writing is one of the core skills that we have to learn and the past few years have seen significant uptake of applications that correct your grammar or even make suggestions of words or sentences that you should write (Johri, 2020). There are also systems in use now that help predict student success based on their prior performance, allowing faculty and advisor to make timely interventions to support student success (Sweeney, Rangwala, Lester & Johri, 2016).…”

Section: Ai and Augmentation Of Sociomateriality For Learningmentioning

confidence: 99%

Augmented sociomateriality: implications of artificial intelligence for the field of learning technology

Johri

2022

Research in Learning Technology

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There has been a conscious effort in the past decade to produce a more theoretical account of the use of technology for learning. At the same time, advances in artificial intelligence (AI) are being rapidly incorporated into learning technologies, significantly changing their affordances for teaching and learning. In this article I address the question of whether introduction of AI and associated features such as machine learning is a novel development from a theoretical perspective, and if so, how? I draw on the existing perspective of sociomateriality for learning and argue that the use of AI is indeed different because AI transforms sociomateriality by allowing materiality to take on characteristics previously associated primarily with a human agent, thereby shifting the nature of the sociomaterial assemblage. In this data and algorithm-driven AI-based sociomateriality, affordances for representation and agency change, thereby modifying representational and relational practices that are essential for cognition. The dualities of data/algorithm, representational/agentic augmentation, and relational/participatory practices act in tandem within this new sociomaterial assemblage. If left unchecked, this new assemblage is prone to perpetuate the biases programmed within the technology itself. Therefore, it is important to take ethical and moral implications of using AI-driven learning technologies into account before their use.

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“…New educational technological solutions, in particular those that draw on big data, machine learning, and artificial intelligence (AI), are only now being deployed on a large scale. The period of early adoption has been characterized by high ambivalence toward the use of data-intensive and AI-driven technologies and provides an opportunity to study user perceptions and experiences (Johri, 2018(Johri, , 2020. To understand this terrain better, we address the following research question: In what ways did students experience VbM during exams as positive, negative, and ambivalent?…”

Section: Introductionmentioning

confidence: 99%

Students' technological ambivalence toward online proctoring and the need for responsible use of educational technologies

Johri

Hingle

2023

J of Engineering Edu

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Background COVID‐19 has led to an unprecedented increase in the use of technology for teaching and learning in higher education institutions (HEIs), including in engineering, computing, and technology programs. Given the urgency of the situation, technologies were often implemented with a short‐term rather than long‐term view. Purpose In this study, we investigate students' perceptions of the use of video‐based monitoring (VbM) for proctoring exams to better assess its impact on students. We leverage technological ambivalence as a framing lens to analyze students' experiences and perceptions of using VbM and draw implications for responsible use of educational technology. Method Qualitative data were collected from students using focus group interviews and discussion board assignments and analyzed inductively to understand students' experiences. Findings We present a framework of how a technological shift of existing practice triggered ambivalence that manifested itself as a sustained negative outlook among students regarding the use of VbM, as well as their institution and instructors. Students accepted the inevitability of the technology but were unconvinced that the benefits of VbM outweighed its risks. Conclusions As instructors use educational technologies that are inherently driven by user data and algorithms that are not transparent, it is imperative that they are attentive to the responsible use of technology. To educate future engineers who are ethically and morally responsible, engineering educators and engineering institutions need to exhibit that behavior in their own practices, starting with their use of educational technologies.

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Artificial intelligence and engineering education

Cited by 18 publications

References 14 publications

Aligning classroom assessment with engineering practice: A design‐based research study of a two‐stage exam with authentic assessment

Aligning classroom assessment with engineering practice: A design‐based research study of a two‐stage exam with authentic assessment

Augmented sociomateriality: implications of artificial intelligence for the field of learning technology

Students' technological ambivalence toward online proctoring and the need for responsible use of educational technologies

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